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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: fashion-clothing-decade |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Fashion Clothing Decade |
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This model predicts what decade clothing is from. It takes an image and outputs one of the following labels: |
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**1910s, 1920s, 1930s, 1940s, 1950s, 1960s, 1970s, 1980s, 1990s, 2000s** |
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### How to use |
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```python |
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from transformers import pipeline |
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pipe = pipeline("image-classification", model="tonyassi/fashion-clothing-decade") |
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result = pipe('image.png') |
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print(result) |
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``` |
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## Dataset |
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Trained on a total of 2500 images. ~250 images from each label. |
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### 1910s |
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### 1920s |
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### 1930s |
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### 1940s |
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### 1950s |
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### 1960s |
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### 1970s |
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### 1980s |
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### 1990s |
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### 2000s |
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## Model description |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k). |
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## Training and evaluation data |
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- Loss: 0.8707 |
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- Accuracy: 0.7505 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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